Summer 2027 Systems Engineering Intern

PDT Partners is recruiting for Summer 2027 Systems Engineering Intern in New York, NY. This listing was last seen on the firm's own job board on August 22, 2026.

A small research fund spun out of Morgan Stanley, hiring a handful of researchers a year.

What sets PDT Partners apart: Very small intake, in single digits most years. Research heritage from Morgan Stanley's process driven trading group. Statistics weighted far above coding speed.

Related preparation: Quantitative Developer (QD) Career Roadmap, Quant Infrastructure Engineer Roadmap, Quantitative Data Engineer Career Roadmap and Algorithmic Trading Developer Career Roadmap.

The role, as PDT Partners describes it, published on July 24, 2026 and reproduced from their job board:

Eligibility: Current Bachelor's, Master's, or PhD students pursuing degrees in rigorous, highly technical fields (e.g., Computer Science, Computer Engineering) who are eligible for full-time roles starting in 2028. Program Length: 10 Weeks (Early June – Mid August) Application Process: Please apply directly through this posting. Applications are reviewed, and candidates will progress to the first-round technical assessment on a rolling basis. You can anticipate receiving feedback on your application within 3 weeks. PDT Partners – a quantitative investment manager - is seeking talented students for internships focused on Infrastructure Engineering (Systems, Network, DevOps, Cloud, etc.). We…

Face problems that don't have well-defined solutions - creativity will be essential to come up with the best possible answers, including ones that explore new technologies

Learn about the infrastructure needed to support a quantitative trading business

Gain experience with multicast market data distribution

Analyze the latency impact of network buffering

Automate the provisioning of cloud infrastructure, including compute, services, and storage

Develop GitOps-driven configuration management for consistent global deployments at scale

Add core components to our cloud-based Kubernetes infrastructure

Ideal candidates have the following knowledge and/or wish to learn more about:

Using software and automation to solve problems

Distributed computing, including hybrid cloud architectures